Overview

Dataset statistics

Number of variables18
Number of observations214
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.8 KiB
Average record size in memory152.0 B

Variable types

NUM16
CAT2

Warnings

numCDR_DATOSB is highly correlated with numDC_CCN and 3 other fieldsHigh correlation
numDC_CCN is highly correlated with numCDR_DATOSB and 2 other fieldsHigh correlation
TPS_Voice is highly correlated with numCDR_CALLBHigh correlation
numCDR_CALLB is highly correlated with TPS_VoiceHigh correlation
TPS_CCNGY_SSU is highly correlated with numDC_CCN and 2 other fieldsHigh correlation
TPS_OCCGY_SSU is highly correlated with numDC_OCC and 1 other fieldsHigh correlation
numDC_OCC is highly correlated with TPS_OCCGY_SSU and 1 other fieldsHigh correlation
TPS_OCCGY_CR is highly correlated with numDC_OCC and 1 other fieldsHigh correlation
TPS_CCNGY_CR is highly correlated with numDC_CCN and 2 other fieldsHigh correlation
percentKPDR is highly correlated with numCDR_DATOSBHigh correlation
date has unique values Unique
numCDR_CONFB has unique values Unique
numCDR_CONFNB has unique values Unique
numCDR_CALLB has unique values Unique
numCDR_CALLNB has unique values Unique
numCDR_DATOSB has unique values Unique
TPS_Voice has unique values Unique
TPS_SMS has unique values Unique
TPS_CCNGY_SSU has unique values Unique
TPS_OCCGY_SSU has unique values Unique
TPS_OCCGY_CR has unique values Unique
TPS_CCNGY_CR has unique values Unique

Reproduction

Analysis started2020-11-18 16:00:15.724275
Analysis finished2020-11-18 16:01:02.222876
Duration46.5 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

date
Categorical

UNIQUE

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2020-08-17
 
1
2020-04-04
 
1
2020-06-15
 
1
2020-07-02
 
1
2020-07-03
 
1
Other values (209)
209 
ValueCountFrequency (%) 
2020-08-1710.5%
 
2020-04-0410.5%
 
2020-06-1510.5%
 
2020-07-0210.5%
 
2020-07-0310.5%
 
2020-06-2410.5%
 
2020-09-0510.5%
 
2020-08-1810.5%
 
2020-08-2210.5%
 
2020-07-0110.5%
 
Other values (204)20495.3%
 
2020-11-18T11:01:02.358554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique214 ?
Unique (%)100.0%
2020-11-18T11:01:02.479227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

numDC_SDP
Real number (ℝ≥0)

Distinct183
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11038.21495
Minimum6094
Maximum15820
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:02.597914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6094
5-th percentile10559.25
Q110667.5
median10772
Q310930.25
95-th percentile12421
Maximum15820
Range9726
Interquartile range (IQR)262.75

Descriptive statistics

Standard deviation966.4442648
Coefficient of variation (CV)0.08755439796
Kurtosis11.0616331
Mean11038.21495
Median Absolute Deviation (MAD)115
Skewness0.7727431221
Sum2362178
Variance934014.517
MonotocityNot monotonic
2020-11-18T11:01:02.732550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1060720.9%
 
1056920.9%
 
1071320.9%
 
1071220.9%
 
1077620.9%
 
1084620.9%
 
1224220.9%
 
1069420.9%
 
1068820.9%
 
1089820.9%
 
Other values (173)19490.7%
 
ValueCountFrequency (%) 
609410.5%
 
613810.5%
 
1046410.5%
 
1051310.5%
 
1051710.5%
 
ValueCountFrequency (%) 
1582010.5%
 
1469110.5%
 
1459010.5%
 
1441510.5%
 
1435310.5%
 

numDC_AIR
Real number (ℝ≥0)

Distinct197
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4588.130841
Minimum3271
Maximum5552
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:02.853406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3271
5-th percentile3877.65
Q14281.25
median4652.5
Q34846.75
95-th percentile5308.35
Maximum5552
Range2281
Interquartile range (IQR)565.5

Descriptive statistics

Standard deviation422.5970474
Coefficient of variation (CV)0.09210658154
Kurtosis-0.1401897837
Mean4588.130841
Median Absolute Deviation (MAD)273
Skewness-0.2494859407
Sum981860
Variance178588.2645
MonotocityNot monotonic
2020-11-18T11:01:02.987631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
474841.9%
 
480231.4%
 
461831.4%
 
449320.9%
 
535720.9%
 
424320.9%
 
490920.9%
 
457820.9%
 
455920.9%
 
468420.9%
 
Other values (187)19088.8%
 
ValueCountFrequency (%) 
327110.5%
 
361110.5%
 
362910.5%
 
368110.5%
 
370310.5%
 
ValueCountFrequency (%) 
555210.5%
 
546310.5%
 
543810.5%
 
541110.5%
 
538510.5%
 

numDC_CCN
Real number (ℝ≥0)

HIGH CORRELATION

Distinct213
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55614.8972
Minimum71
Maximum82323
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:03.117281image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile34794.95
Q141686.5
median54648
Q373151.5
95-th percentile78593.75
Maximum82323
Range82252
Interquartile range (IQR)31465

Descriptive statistics

Standard deviation16656.16219
Coefficient of variation (CV)0.2994910183
Kurtosis-0.7322375606
Mean55614.8972
Median Absolute Deviation (MAD)16289
Skewness-0.1290643427
Sum11901588
Variance277427739
MonotocityNot monotonic
2020-11-18T11:01:03.238955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4582520.9%
 
4351710.5%
 
7439710.5%
 
7289810.5%
 
5599810.5%
 
7903610.5%
 
7700610.5%
 
7902810.5%
 
5393610.5%
 
4062110.5%
 
Other values (203)20394.9%
 
ValueCountFrequency (%) 
7110.5%
 
430010.5%
 
3231610.5%
 
3353610.5%
 
3360210.5%
 
ValueCountFrequency (%) 
8232310.5%
 
8024110.5%
 
8021710.5%
 
7986410.5%
 
7969310.5%
 

numDC_OCC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct209
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10443.3785
Minimum6493
Maximum20743
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:03.364581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6493
5-th percentile7022.1
Q17574
median9311
Q310459.25
95-th percentile19964.3
Maximum20743
Range14250
Interquartile range (IQR)2885.25

Descriptive statistics

Standard deviation4174.598986
Coefficient of variation (CV)0.399736444
Kurtosis0.9634577996
Mean10443.3785
Median Absolute Deviation (MAD)1616.5
Skewness1.523635708
Sum2234883
Variance17427276.7
MonotocityNot monotonic
2020-11-18T11:01:03.723525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
696920.9%
 
2021920.9%
 
773520.9%
 
706220.9%
 
733020.9%
 
718210.5%
 
1365110.5%
 
2012110.5%
 
809610.5%
 
1038710.5%
 
Other values (199)19993.0%
 
ValueCountFrequency (%) 
649310.5%
 
664610.5%
 
669210.5%
 
676710.5%
 
689710.5%
 
ValueCountFrequency (%) 
2074310.5%
 
2045510.5%
 
2040710.5%
 
2036210.5%
 
2035910.5%
 

numCDR_CONFB
Real number (ℝ≥0)

UNIQUE

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3898780.986
Minimum2184513
Maximum6504665
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:03.857124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2184513
5-th percentile2984886.1
Q13395366.25
median3900406
Q34227230.25
95-th percentile5083027.95
Maximum6504665
Range4320152
Interquartile range (IQR)831864

Descriptive statistics

Standard deviation661089.6174
Coefficient of variation (CV)0.1695631583
Kurtosis0.6717795647
Mean3898780.986
Median Absolute Deviation (MAD)434909
Skewness0.4575501166
Sum834339131
Variance4.370394822e+11
MonotocityNot monotonic
2020-11-18T11:01:03.983753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
324607910.5%
 
496558510.5%
 
480634610.5%
 
446688310.5%
 
275812510.5%
 
310492910.5%
 
411820410.5%
 
340805910.5%
 
391186610.5%
 
371474510.5%
 
Other values (204)20495.3%
 
ValueCountFrequency (%) 
218451310.5%
 
252284310.5%
 
261680810.5%
 
263811110.5%
 
265545410.5%
 
ValueCountFrequency (%) 
650466510.5%
 
557864010.5%
 
553772210.5%
 
538693110.5%
 
529245210.5%
 

numCDR_CONFNB
Real number (ℝ≥0)

UNIQUE

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69425176.15
Minimum14018960
Maximum84154650
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:04.113449image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum14018960
5-th percentile63785491.25
Q167196576
median69506268
Q372593027.5
95-th percentile76968998.3
Maximum84154650
Range70135690
Interquartile range (IQR)5396451.5

Descriptive statistics

Standard deviation6339203.039
Coefficient of variation (CV)0.0913098589
Kurtosis42.57862028
Mean69425176.15
Median Absolute Deviation (MAD)2688564
Skewness-5.184586984
Sum1.48569877e+10
Variance4.018549517e+13
MonotocityNot monotonic
2020-11-18T11:01:04.240112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7190579110.5%
 
7698758710.5%
 
6625247710.5%
 
7155423610.5%
 
7036792810.5%
 
6841040210.5%
 
7182866210.5%
 
7736696410.5%
 
7049438710.5%
 
6531294410.5%
 
Other values (204)20495.3%
 
ValueCountFrequency (%) 
1401896010.5%
 
2062489410.5%
 
5924921410.5%
 
5994400810.5%
 
6238967610.5%
 
ValueCountFrequency (%) 
8415465010.5%
 
7931473510.5%
 
7881730910.5%
 
7833109510.5%
 
7789794010.5%
 

numCDR_CALLB
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36904069.8
Minimum24697423
Maximum42742402
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:04.370343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum24697423
5-th percentile29246100.65
Q133863951.5
median38626390
Q339719373.5
95-th percentile41040936.3
Maximum42742402
Range18044979
Interquartile range (IQR)5855422

Descriptive statistics

Standard deviation3919650.262
Coefficient of variation (CV)0.1062118699
Kurtosis-0.08823112565
Mean36904069.8
Median Absolute Deviation (MAD)1707787.5
Skewness-0.9609338111
Sum7897470938
Variance1.536365818e+13
MonotocityNot monotonic
2020-11-18T11:01:04.496059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3831705410.5%
 
3963206210.5%
 
3984249110.5%
 
3907449010.5%
 
4076681410.5%
 
3353976810.5%
 
4023314110.5%
 
4174968410.5%
 
3260126510.5%
 
4045892510.5%
 
Other values (204)20495.3%
 
ValueCountFrequency (%) 
2469742310.5%
 
2540204810.5%
 
2699990610.5%
 
2814059410.5%
 
2829664610.5%
 
ValueCountFrequency (%) 
4274240210.5%
 
4222125310.5%
 
4206298610.5%
 
4204988510.5%
 
4192558410.5%
 

numCDR_CALLNB
Real number (ℝ≥0)

UNIQUE

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7119954.949
Minimum5181087
Maximum8371354
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:04.628668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum5181087
5-th percentile5949903.25
Q16851262.25
median7259054.5
Q37503128.75
95-th percentile7753000.8
Maximum8371354
Range3190267
Interquartile range (IQR)651866.5

Descriptive statistics

Standard deviation558787.2489
Coefficient of variation (CV)0.07848185177
Kurtosis0.4449386727
Mean7119954.949
Median Absolute Deviation (MAD)327468.5
Skewness-0.9452704623
Sum1523670359
Variance3.122431895e+11
MonotocityNot monotonic
2020-11-18T11:01:04.756362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
675941910.5%
 
774895210.5%
 
747804010.5%
 
773906310.5%
 
769158610.5%
 
769502710.5%
 
769666310.5%
 
726084910.5%
 
735812810.5%
 
725726010.5%
 
Other values (204)20495.3%
 
ValueCountFrequency (%) 
518108710.5%
 
546893010.5%
 
582286310.5%
 
586399310.5%
 
587287810.5%
 
ValueCountFrequency (%) 
837135410.5%
 
797224010.5%
 
786903310.5%
 
786708710.5%
 
786413910.5%
 

numCDR_DATOSB
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218136943.8
Minimum156839810
Maximum297993856
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:04.891608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum156839810
5-th percentile168822338.4
Q1181451508.5
median205142786.5
Q3265716570.5
95-th percentile283712159.9
Maximum297993856
Range141154046
Interquartile range (IQR)84265062

Descriptive statistics

Standard deviation42444031.1
Coefficient of variation (CV)0.1945751616
Kurtosis-1.416184851
Mean218136943.8
Median Absolute Deviation (MAD)28643655.5
Skewness0.4430058623
Sum4.668130597e+10
Variance1.801495776e+15
MonotocityNot monotonic
2020-11-18T11:01:05.022259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
21240114710.5%
 
26108947610.5%
 
18776544110.5%
 
18392632010.5%
 
16848461810.5%
 
28733611910.5%
 
27208930910.5%
 
20610270910.5%
 
20507512410.5%
 
17350610810.5%
 
Other values (204)20495.3%
 
ValueCountFrequency (%) 
15683981010.5%
 
15780702010.5%
 
15841063310.5%
 
16556156310.5%
 
16741549310.5%
 
ValueCountFrequency (%) 
29799385610.5%
 
29038738810.5%
 
29019933810.5%
 
28942757510.5%
 
28886423610.5%
 

TPS_Voice
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286024.1342
Minimum172964.0533
Maximum332817.3067
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:05.152911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum172964.0533
5-th percentile234351.295
Q1263807.4442
median298899.015
Q3307656.8308
95-th percentile318905.0423
Maximum332817.3067
Range159853.2533
Interquartile range (IQR)43849.38667

Descriptive statistics

Standard deviation30923.31124
Coefficient of variation (CV)0.1081143426
Kurtosis0.6054806849
Mean286024.1342
Median Absolute Deviation (MAD)13088.905
Skewness-1.100901987
Sum61209164.73
Variance956251178.2
MonotocityNot monotonic
2020-11-18T11:01:05.278332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
297987.843310.5%
 
313238.710.5%
 
326110.303310.5%
 
292400.686710.5%
 
276098.0610.5%
 
237888.123310.5%
 
314363.810.5%
 
299262.363310.5%
 
240768.9910.5%
 
310360.656710.5%
 
Other values (204)20495.3%
 
ValueCountFrequency (%) 
172964.053310.5%
 
191712.3910.5%
 
199055.1810.5%
 
199701.533310.5%
 
205315.926710.5%
 
ValueCountFrequency (%) 
332817.306710.5%
 
332713.7310.5%
 
329141.3410.5%
 
326110.303310.5%
 
324903.653310.5%
 

TPS_SMS
Real number (ℝ≥0)

UNIQUE

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29526.52534
Minimum21797.14667
Maximum55243.93
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:05.409979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum21797.14667
5-th percentile22752.80683
Q125567.12417
median28062.17333
Q332427.13333
95-th percentile39742.10433
Maximum55243.93
Range33446.78333
Interquartile range (IQR)6860.009167

Descriptive statistics

Standard deviation5655.727982
Coefficient of variation (CV)0.1915473601
Kurtosis2.472550272
Mean29526.52534
Median Absolute Deviation (MAD)2718.686667
Skewness1.449373382
Sum6318676.423
Variance31987259
MonotocityNot monotonic
2020-11-18T11:01:05.535646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
33011.410.5%
 
29293.9610.5%
 
43654.7866710.5%
 
25304.6833310.5%
 
29831.7366710.5%
 
28076.2433310.5%
 
28595.1133310.5%
 
29406.4466710.5%
 
28782.8110.5%
 
27760.1733310.5%
 
Other values (204)20495.3%
 
ValueCountFrequency (%) 
21797.1466710.5%
 
22220.1266710.5%
 
22265.2166710.5%
 
22269.5333310.5%
 
22406.0933310.5%
 
ValueCountFrequency (%) 
55243.9310.5%
 
50710.4333310.5%
 
46512.2566710.5%
 
45305.6466710.5%
 
45292.0966710.5%
 

TPS_CCNGY_SSU
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3734780.848
Minimum2351334.833
Maximum5006832.227
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:05.666294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2351334.833
5-th percentile2449833.159
Q13081623.757
median3762218.875
Q34595268.753
95-th percentile4841609.152
Maximum5006832.227
Range2655497.393
Interquartile range (IQR)1513644.996

Descriptive statistics

Standard deviation827270.7264
Coefficient of variation (CV)0.2215044899
Kurtosis-1.354012863
Mean3734780.848
Median Absolute Deviation (MAD)770533.0317
Skewness-0.1143889957
Sum799243101.4
Variance6.843768548e+11
MonotocityNot monotonic
2020-11-18T11:01:05.800154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3834146.44710.5%
 
2806862.73310.5%
 
2439675.5910.5%
 
4512008.22310.5%
 
2445522.3710.5%
 
4344541.910.5%
 
4483312.71710.5%
 
3349739.1910.5%
 
2445384.98710.5%
 
4040602.91710.5%
 
Other values (204)20495.3%
 
ValueCountFrequency (%) 
2351334.83310.5%
 
2400617.16310.5%
 
2412713.62310.5%
 
2421929.76710.5%
 
2439675.5910.5%
 
ValueCountFrequency (%) 
5006832.22710.5%
 
4939485.47710.5%
 
4938451.89710.5%
 
4905056.59310.5%
 
4893735.0810.5%
 

TPS_OCCGY_SSU
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean864763.726
Minimum516180.8367
Maximum1662615.287
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:05.927812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum516180.8367
5-th percentile570140.3805
Q1664940.1983
median779180.7567
Q3877603.1683
95-th percentile1614387.647
Maximum1662615.287
Range1146434.45
Interquartile range (IQR)212662.97

Descriptive statistics

Standard deviation326180.5183
Coefficient of variation (CV)0.3771903336
Kurtosis1.027202504
Mean864763.726
Median Absolute Deviation (MAD)112636.22
Skewness1.520450085
Sum185059437.4
Variance1.063937305e+11
MonotocityNot monotonic
2020-11-18T11:01:06.068401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
790184.7310.5%
 
798587.6810.5%
 
566822.143310.5%
 
570355.4810.5%
 
661322.853310.5%
 
857932.343310.5%
 
754154.226710.5%
 
869442.903310.5%
 
805718.373310.5%
 
881721.0210.5%
 
Other values (204)20495.3%
 
ValueCountFrequency (%) 
516180.836710.5%
 
556180.236710.5%
 
558110.566710.5%
 
558905.2510.5%
 
561120.313310.5%
 
ValueCountFrequency (%) 
1662615.28710.5%
 
1661601.22710.5%
 
1653143.0210.5%
 
1644657.0610.5%
 
1639776.38310.5%
 

TPS_OCCGY_CR
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean869379.1007
Minimum519207.0667
Maximum1673120.187
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:06.194361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum519207.0667
5-th percentile572882.338
Q1668095.955
median782415.3817
Q3882408.2267
95-th percentile1624367.732
Maximum1673120.187
Range1153913.12
Interquartile range (IQR)214312.2717

Descriptive statistics

Standard deviation328651.2915
Coefficient of variation (CV)0.3780298965
Kurtosis1.027784752
Mean869379.1007
Median Absolute Deviation (MAD)112891.9567
Skewness1.521213273
Sum186047127.6
Variance1.080116714e+11
MonotocityNot monotonic
2020-11-18T11:01:06.336983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
790658.796710.5%
 
574684.4510.5%
 
790757.0110.5%
 
845810.2210.5%
 
575594.686710.5%
 
784439.203310.5%
 
1644442.84310.5%
 
775293.453310.5%
 
1646177.110.5%
 
1664085.40710.5%
 
Other values (204)20495.3%
 
ValueCountFrequency (%) 
519207.066710.5%
 
559210.610.5%
 
560434.746710.5%
 
561618.876710.5%
 
563573.396710.5%
 
ValueCountFrequency (%) 
1673120.18710.5%
 
1672256.25310.5%
 
1664085.40710.5%
 
1655275.30710.5%
 
1649990.1410.5%
 

TPS_CCNGY_CR
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3882011.738
Minimum2508410.313
Maximum5132229.053
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:06.464872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2508410.313
5-th percentile2582875.67
Q13266036.247
median3929775.882
Q34724729.822
95-th percentile4953055.646
Maximum5132229.053
Range2623818.74
Interquartile range (IQR)1458693.574

Descriptive statistics

Standard deviation815912.403
Coefficient of variation (CV)0.2101777269
Kurtosis-1.300037826
Mean3882011.738
Median Absolute Deviation (MAD)754603.2517
Skewness-0.1754756444
Sum830750511.9
Variance6.657130493e+11
MonotocityNot monotonic
2020-11-18T11:01:06.600473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4073869.9210.5%
 
3406011.57310.5%
 
4658929.610.5%
 
4957643.26310.5%
 
4935399.35310.5%
 
2581540.96710.5%
 
4567518.6410.5%
 
320379110.5%
 
4052945.12310.5%
 
3416830.50710.5%
 
Other values (204)20495.3%
 
ValueCountFrequency (%) 
2508410.31310.5%
 
2512211.20310.5%
 
2545434.71310.5%
 
2553340.72710.5%
 
2562316.55310.5%
 
ValueCountFrequency (%) 
5132229.05310.5%
 
5054630.71310.5%
 
5041569.87710.5%
 
501956710.5%
 
5008525.3210.5%
 

percentKPDR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct207
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.6611215
Minimum69.32
Maximum114.3
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2020-11-18T11:01:06.735255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum69.32
5-th percentile75.7995
Q181.0975
median90.2
Q3103.3825
95-th percentile108.727
Maximum114.3
Range44.98
Interquartile range (IQR)22.285

Descriptive statistics

Standard deviation11.49147901
Coefficient of variation (CV)0.1253691731
Kurtosis-1.239122913
Mean91.6611215
Median Absolute Deviation (MAD)10.655
Skewness0.1688081334
Sum19615.48
Variance132.0540898
MonotocityNot monotonic
2020-11-18T11:01:06.857930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
106.3331.4%
 
90.1420.9%
 
79.6320.9%
 
108.1720.9%
 
90.420.9%
 
78.6820.9%
 
78.4510.5%
 
102.1110.5%
 
73.1610.5%
 
92.0510.5%
 
Other values (197)19792.1%
 
ValueCountFrequency (%) 
69.3210.5%
 
70.2510.5%
 
70.610.5%
 
72.7110.5%
 
73.1610.5%
 
ValueCountFrequency (%) 
114.310.5%
 
112.8310.5%
 
112.2610.5%
 
111.0710.5%
 
110.9410.5%
 

Estado
Categorical

Distinct3
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ERROR
111 
WARNING
59 
OK
44 
ValueCountFrequency (%) 
ERROR11151.9%
 
WARNING5927.6%
 
OK4420.6%
 
2020-11-18T11:01:06.978635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-18T11:01:07.047453image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:07.124251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length5
Mean length4.934579439
Min length2

Interactions

2020-11-18T11:00:24.088937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:24.338270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:24.489902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:24.636514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:24.778133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:24.894994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:25.022620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:25.190171image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:25.360717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:25.592102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:25.878774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:26.123121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:26.275277image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:26.427869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:26.563131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:26.683842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:26.803291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:26.948901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:27.162162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:27.298769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:27.419478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:27.530849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:27.640100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:27.762805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:27.913371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:28.073850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:28.224528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:28.386132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:28.536727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:28.663394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:28.778085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:28.892617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:29.022270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:29.149962image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:29.267648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:29.388292image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:29.506245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:29.661066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:29.881477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:30.184665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:30.448960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:30.685326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:30.870831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:30.994532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:31.115717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:31.235363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:31.364020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:31.476751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:31.582471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:31.710143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:31.928527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:32.089633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:32.326998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:32.470419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:32.603066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:32.755909image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:32.907506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:33.098508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:33.248109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:33.376318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:33.500987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:33.627812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:33.763452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:33.880138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:34.024753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:34.166409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:34.291042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:34.430690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:34.550405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:34.664100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:34.784783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:34.918761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:35.047451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:35.240056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:35.377724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:35.496370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:35.618047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:35.762758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:35.889441image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:36.014066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:36.129757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:36.263400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:36.408014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:36.535671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:36.681282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:36.808941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:37.065256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:37.245774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:37.524033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:37.781344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:38.155344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:38.708864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:38.913317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:39.355135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:39.568565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:39.835437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:40.206445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:40.399966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:40.613981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:40.863281image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:41.075727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:41.326041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:41.509161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:41.669241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:41.831871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:42.096200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:42.331545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:42.517216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:42.674307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:42.840863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:43.003428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:43.335541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:43.676629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:43.867122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:44.018750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:44.219182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:44.407674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:44.561809image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:44.707376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:44.846968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:44.979652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:45.117280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:45.288786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:45.476286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:45.610596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:45.736221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:45.872856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:46.024487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:46.140141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-11-18T11:00:46.394462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:46.528138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:46.655764image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:46.770492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:46.888180image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:47.007860image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:47.123552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:47.247487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:47.373152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:47.488846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:47.608486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:47.730213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:48.019133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:48.153772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:48.279017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:48.401687image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:48.530422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:48.664061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:48.809160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:48.929871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:49.050105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:49.185739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:49.321861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:49.447530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:49.578175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:49.722753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:49.877793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:50.024384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:50.184526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:50.363921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:50.524489image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:50.674126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:50.828711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:50.947394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:51.096960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:51.225649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:51.339311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:51.463061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:51.590719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:51.728353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:51.882971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:52.003651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:52.113363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:52.226697image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:52.340430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:52.450105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:52.553857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:52.671542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:52.780254image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:52.883978image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:52.995617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:53.103295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:53.220643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:53.340322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:53.450065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:53.562819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:53.677480image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:53.783888image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:53.891601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:54.003338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:54.106609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:54.215354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:54.316490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:54.420213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:54.520904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:54.619645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:54.728388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:54.825127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:54.938495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:55.061127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:55.192816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:55.332437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-11-18T11:00:55.811156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:55.940777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:56.071427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:56.188153image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:56.302810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:56.410570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:56.521241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:56.626956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:56.740165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:56.880789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:57.005455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:57.125135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:57.248844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:57.365529image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:57.477230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:57.605848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:57.719577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:57.825298image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:57.930123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:58.034844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:58.145548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:58.244287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:58.348968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:58.464660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:58.576361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:58.687649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:58.788336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:58.895052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:59.006790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:59.120484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:59.230191image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:59.345007image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:59.452716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:59.557400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:59.664154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:59.771376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:59.875102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:00:59.981818image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:00.092041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:00.186960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:00.288685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:00.404374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:00.507100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:00.611787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:00.724521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:00.828243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:00.934923image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:01.040677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:01.138729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:01.234473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:01.327224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:01.419979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:01.512237image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-11-18T11:01:07.230032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-18T11:01:07.448446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-18T11:01:07.679789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-18T11:01:07.902776image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-18T11:01:01.744404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-11-18T11:01:02.057052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

datenumDC_SDPnumDC_AIRnumDC_CCNnumDC_OCCnumCDR_CONFBnumCDR_CONFNBnumCDR_CALLBnumCDR_CALLNBnumCDR_DATOSBTPS_VoiceTPS_SMSTPS_CCNGY_SSUTPS_OCCGY_SSUTPS_OCCGY_CRTPS_CCNGY_CRpercentKPDREstado
02020-04-01141614243525327360404091763984419284338546811561194660821239041.40000029518.4200003.575887e+06683569.826667686378.6566673.725133e+0679.34OK
12020-04-02105744140527217341330577663328979285088796689355195071691238645.59333331343.5800003.572376e+06674990.130000677777.2566673.718957e+0679.30OK
22020-04-03105694237530897380334162066113477289659496761676195942549241237.90333331371.5733333.587192e+06679681.506667682648.0700003.729649e+0680.18WARNING
32020-04-04106064257515467306319117973612433282966466473602190947487227801.90333331313.6966673.512402e+06674399.726667677520.7166673.646235e+0679.47OK
42020-04-05105973745501347105261680872131779254020485863993183873713199701.53333330047.8366673.437706e+06666307.100000669164.1200003.577429e+0676.46OK
52020-04-06121774477500007062332702676450741305697976927210184840742237030.92333337293.5400003.427868e+06660622.006667663632.8433333.569179e+0678.48OK
62020-04-07108524645515247297400876584154650341565397292293191536659250539.61333350710.4333333.498902e+06665854.046667669410.1366673.633378e+0682.74WARNING
72020-04-0861384475553157289357816820624894349601887332139200813987251921.09666755243.9300003.701817e+06669876.396667670953.5700003.889830e+0675.78OK
82020-04-0960943681582067401265545414018960284766265939734205210449199055.18000045305.6466673.861530e+06690223.816667690962.2966674.059379e+0675.15OK
92020-04-10105563271574207664218451359249214246974235468930204189458172964.05333339641.3300003.807117e+06728110.320000730760.7366673.976372e+0680.49WARNING

Last rows

datenumDC_SDPnumDC_AIRnumDC_CCNnumDC_OCCnumCDR_CONFBnumCDR_CONFNBnumCDR_CALLBnumCDR_CALLNBnumCDR_DATOSBTPS_VoiceTPS_SMSTPS_CCNGY_SSUTPS_OCCGY_SSUTPS_OCCGY_CRTPS_CCNGY_CRpercentKPDREstado
2042020-10-221087546743478019840380357871903983386112037483252183926320298300.21666725244.9366672.446786e+061.613813e+061.623819e+062.586208e+0690.40ERROR
2052020-10-231087847683524419958390157072680714396723457781116185419220307155.31333326321.2700002.481725e+061.639776e+061.649990e+062.623532e+0691.13ERROR
2062020-10-241089849953520219935423974473160729399716727708153186184363314145.42333326449.3933332.492716e+061.636901e+061.647467e+062.624942e+0690.99ERROR
2072020-10-251089941943360219335339491068313714331171786912942176728096275347.72333323786.6166672.412714e+061.591083e+061.600180e+062.545435e+0686.04WARNING
2082020-10-261252753573489719702454020874287280394481487861047184372235314217.57666727026.3466672.458325e+061.606183e+061.616931e+062.585371e+0691.08ERROR
2092020-10-271115948223488719525492502878817309387558497705452184821365301677.71333326820.5300002.454138e+061.581382e+061.591777e+062.581541e+0691.29ERROR
2102020-10-281095447553480319629396463171964617386343267500800183658645299805.73666727102.7533332.445385e+061.589553e+061.599452e+062.570121e+0690.14ERROR
2112020-10-291096448083471519582399561373594474391225367201482183164716303930.37666726924.4966672.449835e+061.591896e+061.602344e+062.571330e+0690.18ERROR
2122020-10-301090848263429819574395304672855536403994018371354182589886317773.91333326523.0600002.439676e+061.593849e+061.608167e+062.564460e+0690.22ERROR
2132020-10-311097054633498120219469344372542360408987187748952188909545324903.65333325778.6266672.477592e+061.644657e+061.655275e+062.597136e+0691.83ERROR